Interest in the field of the natural control of human limbs using physiological signals has risen dramatically in the past 20 years due to the success of the brain-machine interface. Cortical signals carry significant information, but are difficult to access. The peripheral nerves of the body carry both command and sensory signals, and are far more accessible. While numerous studies have documented the selective stimulation properties of conventionally round electrodes, nerve cuff electrodes (i.e., transverse geometry), and even self-sizing electrodes, recording the activity levels from individual fascicles using these electrodes is still an unsolved problem.
Functional electrical stimulation (FES) can restore volitional motion to patients with neurological injuries or diseases using electrical stimulation of nerves innervating the muscles to be independently controlled. Developing a motion control algorithm for FES, however, is a challenging problem due to inherent complexities of musculoskeletal systems (e.g., highly nonlinear, strongly coupled, time-varying, time-delayed, and redundant properties) and the large number of channels required to activate the various muscles involved in the motion. Additionally, the localization and recovery of many signals poses a significant challenge due to the signal-to-noise ratio and the large number of fascicles.